Computer-implemented land planning system and method designed to generate at least one conceptual fit solution to a user-defined land development problem

ABSTRACT

A computer-implemented land planning system is designed to generate at least one conceptual fit solution to a user-defined land development problem. The system electronically creates at least one candidate solution to the land development problem. The candidate solution incorporates a number of engineering measurements applicable in development of an undeveloped land site. A fitness function quantitatively evaluates the candidate solution based on its fitness. A heuristic problem-solving strategy manipulates the engineering measurements of the candidate solution to achieve a more quantitatively fit solution to the land development problem. A computer output device outputs to a user documentation illustrating the fit solution to the land development problem.

TECHNICAL FIELD AND BACKGROUND OF THE INVENTION

This invention relates to a computer-implemented land planning systemand method designed to generate at least one conceptual fit solution toa user-defined land development problem. The invention is equallyapplicable to the planning and development of single and multi-padcommercial, mixed use, and residential land sites.

The process used today by professional real estate developers,corporations, government entities and others to assess land forengineering feasibility, cost of developing, and investment purposes istime consuming, inaccurate, and expensive. Unfortunately, the currentprocess is getting even more complex and expensive due to addedbureaucratic complications with land use zoning, environmentalprotection requirements, extended permitting processes as well as theavailability and escalating cost of land in desirable areas. Thisproblem affects a broad spectrum of land users including, for example,real estate developers (office/industrial, commercial, retail,residential), corporations which own and use real estate(public/private), and government entities (Federal, State, County,City).

For each of the above users, assessing the feasibility of a land sitefor development typically involves a land development team including oneor more architects, engineers, and land planners. Many of these teammembers are engaged to layout and plan the intended uses on the sitebeing considered. This initial planning process can take from 2 days tofour weeks, and usually results in a single schematic drawing withlimited information (e.g., will the site support the building footprintsor building lots and the necessary streets and/or parking lots?). Atthis point, based largely on intuition and a “gut feeling” about theproject, the developer will choose to contract for additional planningand engineering to more accurately assess the feasibility of the planand the budget. This process can take 2 weeks to 16 weeks and usuallyresults in only one option that is based on the designer's experiencebut is not optimized in any respect. This information is then used toestimate a more accurate budget. Often times value engineering isrequired to bring the design back within the original budget. Thisprocess takes 2 weeks to 6 weeks. The final budget is not generallydetermined until the end of the planning process—some 3-4 mos afterinitial consideration of the land site.

The above planning process often must occur before the property ispurchased, and requires substantial investment in legal fees and earnestmoney to hold the property for an extended length of time.

After this 4 week to 28-week process (average 16 weeks) and considerableexpense and risk of lost opportunity, the developer must assess the riskof purchasing and developing the property based on one unoptimizeddesign option. Unfortunately, the process outlined above is complicatedeven further by miscommunication and disconnect between the many groupsinvolved, which often results in bad designs, bad budgets,disagreements, and bad projects.

The present applicant recognized that the land development industryneeds a major paradigm shift, which is now possible through advances inmathematical modeling and computing hardware. One primary goal of thepresent invention is to fix the problems outlined above through avirtual engineering system that can produce many optimized alternativesfor land development—including the planning, engineering, and budgetingof each potential solution. This computing process is generally achievedin a maximum 24-hour period.

Heuristic Strategy

The speed and effectiveness of the present invention is advanced using aheuristic mathematical approach, such as genetic algorithms. Conciselystated, a genetic algorithm (or “GA”) is a programming technique thatmimics biological evolution as a problem-solving strategy. Given aspecific problem to solve, the input to the GA is a set of potentialsolutions to that problem, encoded in some fashion, and a metric calleda fitness function that allows each candidate to be quantitativelyevaluated. These candidates may be solutions already known to work, withthe aim of the GA being to improve them, but more often they aregenerated at random.

From these initial candidate solutions, random changes are introducedthrough processes known as mutation and crossover. The resulting digitaloffspring then go on to the next generation, forming a new pool ofcandidate solutions, and are subjected to a second round of fitnessevaluation. Those candidate solutions which were worsened, or made nobetter, by the changes to their code are again deleted; but again,purely by chance, the random variations introduced into the populationmay have improved some individuals, making them into better, morecomplete or more efficient solutions to the stated problem. Again thesewinning individuals are selected and copied over into the nextgeneration with random changes, and the process repeats. The expectationis that the average fitness of the population will increase each round,and so by repeating this process for hundreds or thousands of rounds,very good solutions to the problem can be discovered.

SUMMARY OF INVENTION

Therefore, it is an object of the invention to provide acomputer-implemented land planning system and method designed togenerate at least one conceptual fit solution to a user-defined landdevelopment problem.

It is another object of the invention to provide a computer-implementedland planning system which utilizes a heuristic problemsolving-strategy, such as genetic algorithms. According to the presentgenetic algorithm, the evolution starts from a population of completelyrandom individuals and happens in generations. In each generation, thefitness of the whole population is evaluated, multiple individuals arestochastically selected from the current population (based on theirfitness), modified (mutated or recombined) to form a new population,which becomes current in the next iteration of the algorithm.

It is another object of the invention to provide a computer-implementedland planning system which performs land planning and engineeringsimultaneously. This invention considers all land development parameters(e.g., site specifications, user constraints, cost information) up frontfrom both the land planner and the engineer perspective, and thenexplores millions of options using heuristic algorithms to determinewhich options are best as determined by cost and revenue.

It is another object of the invention to provide a computer-implementedland planning system which applies a heuristic problem-solving strategyto the current civil engineering process to revolutionize residentialand commercial land planning and development.

It is another object of the invention to provide a computer-implementedland planning system which shortens the time it takes to get a finalengineering drawing (85% complete or more), including cost information,from 3-4 months to less than 24 hours in many cases.

It is another object of the invention to provide a computer-implementedland planning system which provides technology, accessible via the web,which will enable a user to determine the most cost-effective way todevelop a land site.

It is another object of the invention to provide a computer-implementedland planning system which enables visualization of a land developmentproblem and the ultimate solution.

It is another object of the invention to provide a computer-implementedland planning system which gives the land developer direct access toqualified information in roughly 24 hours (or less) versus many months.

It is another object of the invention to provide a computer-implementedland planning system which minimizes the initial investment capitalrequired for developing a land site.

It is another object of the invention to provide a computer-implementedland planning system which lowers engineering costs.

It is another object of the invention to provide a computer-implementedland planning system which minimizing the risk associated withdeveloping a land site.

It is another object of the invention to provide a computer-implementedland planning system which optimizes engineering time.

It is another object of the invention to provide a computer-implementedland planning system which effectively integrates the creative(aesthetics) and engineering sides of land planning and development toachieve a globally optimal solution.

It is another object of the invention to provide a computer-implementedland planning system which optimizes around financial measurements, suchas cost and return on investment (ROI).

It is another object of the invention to provide a computer-implementedland planning system which generates multiple “optimally different”solutions to a land development problem, and which presents thesolutions in a “.dwg” format that can be input and manipulated directlyinto an engineers' existing CAD/CAM system.

It is another object of the invention to provide a computer-implementedland planning system which is available for use on stand-alone PCs ornetworks.

It is another object of the invention to provide a computer-implementedland planning system which utilizes Digital Satellite Topography.

It is another object of the invention to provide a computer-implementedland planning system which utilizes a heuristic problem-solving strategycapable of manipulating many parameters simultaneously.

It is another object of the invention to provide a computer-implementedland planning system which utilizes a heuristic problem-solving strategywhich searches beyond the local optima.

It is another object of the invention to provide a computer-implementedland planning system which utilizes a heuristic problem-solving strategydesigned to find the global optimum in a space with many local optima.

It is another object of the invention to provide a computer-implementedland planning system which utilizes a heuristic problem-solving strategyapplicable in traffic engineering including signal optimization andhighway design.

It is another object of the invention to provide a computer-implementedland planning system which utilizes a heuristic problem-solving strategyapplicable for optimizing the structural design of buildings andbridges.

These and other objects of the present invention are achieved in thepreferred embodiments disclosed below by providing acomputer-implemented land planning system designed to generate at leastone conceptual fit solution to a user-defined land development problem.The system employs a computer readable medium and a computer programencoded on the medium. The computer program is operable, when executedon a computer, for electronically creating at least one candidatesolution to the land development problem. The candidate solutionincorporates a plurality of engineering measurements applicable indevelopment of an undeveloped land site. A fitness functionquantitatively evaluates the candidate solution based on its fitness. Aheuristic problem-solving strategy manipulates the engineeringmeasurements of the candidate solution to achieve a more quantitativelyfit solution to the land development problem. An output means, such as adisplay monitor, printer, electronic communication, or the like,delivers to a user documentation illustrating the fit solution to theland development problem.

The term “planning” is defined broadly herein to refer to any conceptualdevelopment of a land site. The term “undeveloped land site” refers to asite which may or may not have existing structure and/or engineeringinfrastructure, and which is not yet developed according to one of theconceptual fit solutions generated in the present system. The term“heuristic” refers broadly to any problem-solving strategy that utilizesself-educating techniques (as the evaluation of feedback) to improveperformance. The following are examples of heuristic problem-solvingstrategies: evolutionary algorithms, such as genetic algorithms,simulated annealing, neural networks, hill climbing, Ant Colonyoptimization, Particle Swarm optimization, and tabu search.

According to another preferred embodiment, means, such as a digitalterrain model, digitally represents the undeveloped land site inthree-dimensional space.

According to another preferred embodiment, a computer program comprisesinstructions for conceptually locating the engineering measurementswithin the three-dimensional space.

According to another preferred embodiment, the engineering measurementsare selected from a group including, but not limited to, storm watersystem, sanitary sewer collection system, and potable water system.

According to another preferred embodiment, the output documentationcomprises a least one computer-generated drawing.

According to another preferred embodiment, the output documentationfurther comprises an itemized cost listing of the engineeringmeasurements.

According to another preferred embodiment, the documentation isdelivered to the user via a global communications network.

In another embodiment, the invention is a computer-implemented landplanning system designed to generate at least one conceptual fitsolution to a user-defined land development problem. A processoraccesses land development constraints for an undeveloped land site. Thesystem further employs a computer readable medium and a computer programencoded on the medium. The computer program is operable, when executedon a computer, for creating a population of candidate solutions to theland development problem. Each candidate solution includes a pluralityof engineering measurements applicable in development of the undevelopedland site. The processor accesses a cost model including respective costdata for each of the engineering measurements. A computer programcomprises instructions for discarding unfit solutions which violate theland development constraints. For each remaining solution, a fitnessfunction is employed for calculating a fitness score based on the costdata for the engineering measurements. A heuristic problem-solvingstrategy manipulates the engineering measurements of respective selectcandidate solutions to achieve increased fitness scores, such that thosecandidate solutions achieving increased fitness scores compriserespective fit solutions. A computer program comprises instructions forselecting a set of optimally different alternative solutions from theplurality of fit solutions. An output means, such as a display monitor,printer, electronic communication, or the like, is employed fordelivering to a user documentation illustrating the optimally differentalternative solutions to the land development problem.

According to another preferred embodiment, the processor accesses userpreferences for the undeveloped land site.

According to another preferred embodiment, a computer program comprisesinstructions for penalizing the fitness score of a candidate solutionbased on violation of a user preference.

In yet another embodiment, the invention is a computer-implemented landplanning method designed to generate at least one conceptual fitsolution to a user-defined land development problem. The method includesthe steps of electronically creating at least one candidate solution tothe land development problem. The candidate solution comprises aplurality of engineering measurements applicable in development of anundeveloped land site. The candidate solution is evaluatedquantitatively based on its overall fitness. A heuristic problem-solvingstrategy is then employed for manipulating the engineering measurementsof the candidate solution to achieve a more quantitatively fit solutionto the land development problem. After achieving a more fit solution,documentation illustrating the fit solution to the land developmentproblem is output to the user.

BRIEF DESCRIPTION OF THE DRAWINGS

Some of the objects of the invention have been set forth above. Otherobjects and advantages of the invention will appear as the descriptionproceeds when taken in conjunction with the following drawings, inwhich:

FIG. 1 is a schematic overview of the present land planning systemaccording to one preferred embodiment of the invention;

FIG. 2 is a plan view of the land site showing raw survey data atvarious points;

FIG. 3 is perspective view of the land site showing the TriangulatedIrregular Network (TIN);

FIGS. 4-10 are computer screen shots showing various input fields usedfor building the cost model;

FIG. 11 is a flow diagram demonstrating basic operation of the geneticalgorithm employed in the present system; and

FIG. 12 is a computer-generated drawing of the developed land siteaccording to one optimized fit solution.

DESCRIPTION OF THE PREFERRED EMBODIMENT AND BEST MODE

Referring now specifically to the drawings, a computer-implemented landplanning system according to the present invention is representedbroadly in the schematic diagram of FIG. 1. The system employs aheuristic mathematical strategy to generate a set of globally-optimizedsolutions to a complex land development problem. In one embodimentdescribed below, the problem is expressed in terms of optimizing landdevelopment based on costs and budget constraints. Alternatively, thesystem may focus on other economic considerations such as return oninvestment (ROI). The example discussed herein relates to the planningand development of a single pad commercial site. The present concept,however, is equally applicable to the planning and development ofmulti-pad commercial, mixed use, and residential sites.

I. System Overview

In one embodiment, the present system 10 operates in an environmentutilizing a client device 11 in communication with a host server 12 overa computer network 14, such as the Internet. In other embodiments, othercomputer networks, for example, a wide area network (WAN), local areanetwork (LAN), or intranet, may be used.

The host server 12 comprises a processor 15 and a computer readablemedium 16, such as random access memory (RAM). The processor 15 isoperable to execute certain heuristic problem-solving programs 17 storedin memory. Such processors may comprise a microprocessor, or any otherprocessor. Such processors may also communicate with othercomputer-readable media that store computer program instructions, suchthat when the stored instructions are executed by the processor, theprocessor performs the steps described herein.

The problem-solving programs 17, discussed further below, and utilize,as inputs, data from a data storage device 18. In one embodiment thedata storage device 18 comprises an electronic database. In otherembodiments, the data storage device 18 may comprise an electronic file,disk, or other data storage device. The data storage device 18 may storeengineering and cost modules, building codes and regulations, user data,and a repository. The data storage device 18 may also include otheritems useful to carry out the functions of the present system.

in one example, the problem-solving programs 17 comprise one or moregenetic algorithms to “solve” a high level problem statement 19 definedby the user—e.g., optimizing land development at a site based on cost.The system 10 employs Digital Terrain Modeling to create athree-dimensional representation (DTM) of the land site. Certain costmeasurements, discussed below, are then conceptually located within theDTM. The genetic algorithm 17 is used to optimize the design of the sitebased on these cost measurements. The genetic algorithm 17 evolvesmultiple fit solutions to the land development problem 19. These fitsolutions are then interrogated and electronically filtered to achieve asmall number of very different alternative solutions 21, 22, and 23. Theresulting alternatives 21, 22, 23 are transferred over the computernetwork 14 to the client device 11. The user is then able to decidewhich fit solution 21, 22, 23 best satisfies his or her design goals.

II. Cost Measurements

In the present example, each candidate solution is defined by five costmeasurements which are scored individually for fitness based on apredetermined quantitative scale. The score or “fitness index” for eachmeasurement is then totaled to evaluate the overall fitness of the givensolution. The cost measurements comprise: the building pad, parking lotand access roads, storm water collection system, sanitary sewercollection system, and potable water system. Each measurement isconceptually located on the site and engineered based on systemparameters and user-defined hard and soft constraints, discussed below.

For locating the building pad, examples of factors influencing landdevelopment costs include initial site grading, demolition, clearing &grubbing, bulk excavation, fill placement (backfilling), retainingwalls, erosion control, and finish grading. Site grading is generallythe first task undertaken prior to construction, after whichinstallation of utilities, paving, and building construction may begin.Unless the site is in an undeveloped state prior to construction, a costwill be incurred for the demolition of existing structures. Clearingconsists of felling trees and removing and disposing of brush from thedisturbed area prior to construction. Grubbing is the removal anddisposal of stumps and roots. In some cases, a cost will also beincluded for topsoil stripping and stockpiling on site for later usage.

Bulk excavation involves the removal of earth for the construction ofsite features, and has the effect of lowering the elevation of theground surface. Suitable material removed from excavation on site may beused to form embankments, fills, sub-grades, shoulders, backfills andsite grading (i.e., to raise the grade). Cut and fill is preferablybalanced on a given site. A cost may be incurred for transporting fillmaterial from an excavation area to the fill area.

Retaining walls are constructed to prevent erosion and/or structuralinstability of excessively steep embankments. While there is a widevariety of retaining walls including timer tie, stone walls, reinforcedmasonry retaining wall with poured concrete footings, and segmentalretaining wall systems, the type of wall used is largely a function ofits required height and engineer preferences. A given retaining wall'smaximum design height is a function of the retaining wall type and localsoil and groundwater conditions. The present system will determine thelocation, required height, and length of any retaining walls on thesite.

Typical erosion control practices during construction include siltfences, stabilized construction entrances, temporary seeding andmulching, inlet protection, check dams, and temporary sediment basins.Appropriate erosion control will be designed, and costs incurred, basedupon the (x,y,z) coordinates of the disturbed area.

The system is designed to finish off all disturbed areas on the site toa uniformly smooth surface, free from abrupt or irregular surfacechanges. Included in this task is the redistribution of stockpiledtopsoil as necessary in landscaped areas. Finished areas may later belandscaped or paved.

For the second measurement of the candidate solution, locating anddesigning the parking lot and access road(s) is a major cost componentand driving force in the overall design for any single pad commercialsite. Parking lot design guidelines are well established, and may beimported from existing databases. Parking lots are typically paved withasphalt comprising a base course of compacted stone (say 6″ thick), andsurface of asphalt (say 4″ thick). The thickness of the various coursesis a function of traffic loading and desired design life. Curb andgutter will be provided throughout the perimeter of the parking lot andany median island. In addition, a landscaping cost (e.g., for seedingand sodding) will be incurred for any area disturbed during constructionthat is not paved.

The cost measurement for storm water collection system includesproviding storm water structures (e.g., drop inlets) and piping and bestmanagement practices (BMP's), if required. The primary costs associatedwith the drainage infrastructure are those associated with installingthe pipe and structures. A storm water engineering ‘model’ may beutilized to determine the size and location of the various storm waterfacilities and associated quantities based on local site conditions(e.g. finished topography and rainfall) and standard engineeringpractices.

The measurement for sanitary sewer collection system comprises costsincurred for providing sanitary sewer service to the site. In mostcases, an existing sanitary sewerage system will be available to connectto and will be located in an adjacent sewer easement. Sewer easementsare typically located in the road's right-of-way (ROW). In the simplestcase, for small single pad commercial sites, all that will be necessaryis to install a sanitary sewer service lateral (say 6″ diameter PVCmaterial), including a cast iron cleanout, from the building to theexisting trunk sewer in the street. In other cases, if wastewater flowsoriginating at the facility are expected to be relatively high or if thebuilding is far from the right-of-way it will be necessary to lay somenew trunk sewer including manholes.

For potable water system, this measurement comprises costs incurred forproviding drinking water, fire suppression and site irrigation to theland site. The is accomplished by connecting to the municipal or privatewater works. Like sanitary sewers, an existing water main is usuallylocated in an adjacent water easement, which my be in the road ROW. Thebuilding is connected via a service connection, with at least one valve,a water meter, and a service tap at the water main completes the setup.

III. Digital Terrain Model (DTM)

The first step in implementing the present system is to define the sitein three-dimensional “space.” This is achieved through Digital TerrainModeling using any suitable commercially available software such asSurface Modeling™ by Eagle Point Software, MGE Terrain Analyst™ byIntergraph Corporation, or GWN-DTM™ by Scientific Software Group.

Digital Terrain Modeling is an electronic process of representingtopography in three dimensions. The process utilizes raw survey datagathered at certain points chosen to accurately reflect site conditions,and through a Triangulated Irregular Network (TIN) converts this rawsurvey data into data used to effectively represent the site topography.FIGS. 2 and 3 illustrate the conversion of raw survey data (FIG. 2) intoa three-dimensional representation of the site (FIG. 3). In the TIN, anythree raw (x,y,z) coordinates define a finite triangular surface, witheach vertex of the triangle representing an actually measured datapoint. A large series of these finite surfaces, sharing commonhorizontal edges, are linked together in the network and used tointerpolate the (x,y,z) coordinate of any point, even though actualmeasurements have not been obtained at that point.

The TIN models the entire land surface of the site including boundariesand breaklines. Delaunay triangulation rules are followed inpartitioning the points into the nodes of triangles. Concisely stated,all raw survey points are connected with their two nearest neighbors toform triangles. One of the main advantages of such triangles is thatthey are equiangular. It also ensures that any point on the surface isas close as possible to an actually measured survey point.

In the present system, each of the five cost measurements is representedin the DTM by a set of (x,y,z) coordinates—the set indicating northing,easting, and elevation of all points of the measurement in the site. Theprecise location of the measurement in the DTM determines its cost basedon the cost model discussed below.

IV. Rules of Selection

Before application of the cost model, all candidate solutions must meetcertain threshold requirements including those inherent in the systemand those established ad hoc by the user. Collectively, theserequirements or constraints define rules of selection dictating whichcandidate solutions survive initial scrutiny for further considerationand possible regeneration. System constraints may include, for example,engineering parameters such as the depth of cover on water mains, depthof cover of sewer mains, minimum slope of sanitary sewer, pavementdesign requirements, flood plain and wetland area restrictions, minimumditch designs, manhole dimensions, maximum/minimum grades, and othersdictated by applicable engineering models. Other system constraints maycomprise those stored electronically in databases containing applicablemunicipal codes for a given jurisdiction. These databases may includeparameters such as street planting yards, side and rear planting yards,building setbacks, parking space sizes, driveway widths, roadway widths,water pipe materials, sewer pipe materials, and radius of entrances curblines.

User-input constraints are both “hard” and “soft” —hard constraintscomprising required or established parameters while soft constraintsrepresent only preferences. Hard constraints include, for example,building pad design/dimensions, location of existing water line,location of existing sanitary sewer collection system, location of floodplain/wetland areas, and existing utility easements. Examples of userpreferences or soft constraints include width of sidewalks, parkingangles (e.g., 60 or 90 degrees), bike paths, and aestheticconsiderations, such as frontage visibility (pad orientation), greenspace, and preservation of existing landscape.

V. Cost Model

The cost model is entered into the system using a series of GUI screens,such as those shown in FIGS. 4-10. Alternatively, the cost model may beimported from one or more databases or other data storage media. Thecosts included are those that have an engineering basis that can bemodeled. Other fixed costs and fees are ignored in the cost model asthey can be added in during a post-processing step to give a completefinal cost estimate for the site. Preferably, the cost modelincorporates the following data outlined in Tables 1, 2, 3, and 4 below.

TABLE 1 Grading Unit Costs Clearing and grubbing $/SY Topsoil stripping$/CY Bulk common earth excavation $/CY Rock excavation $/CY Excavationof unsuitable material $/CY Fill material $/CY Compaction of fillmaterial $/CY Borrow Material $/CY Disposal of waste material $/CYRetaining wall (<5 ft tall) $/SF Retaining wall (5-10 ft tall) $/SFRetaining wall (>10 ft tall) $/SF Finish grading $/acre Paving forparking/access road for given thickness $/SY Concrete sidewalk $/SY Curband gutter $/LF Seeding and mulching (or sod) $/SY Permanent erosioncontrol fabric for steep slopes $/SY Erosion control during construction$/acre

TABLE 2 Storm Water System Underground detention storage $/CY RCP stormwater pipe of each size (<5 ft deep) $/LF RCP storm water pipe of eachsize (5-10 ft deep) $/LF RCP storm water pipe of each size (>10 ft deep)$/LF Flared end section (FES) of each size $/EA Unit cost per volume ofrip rap $/CY Storm water manholes of each size (<5 ft deep) $/EA Stormwater manholes of each size (5-10 ft deep) $/EA Storm water manholes ofeach size (>10 ft deep) $/EA Curb inlets (<5 ft) $/EA Curb inlets (5-10ft deep) $/EA Curb inlets (>10 ft deep) $/EA Drop inlets (<5 ft) $/EADrop inlets (5-10 ft deep) $/EA Drop inlets (>10 ft deep) $/EA Stormwater lift stations $/EA Storm water lift station force main of eachsize $/LF

TABLE 3 Sanitary Sewer Collection System Sanitary sewer pipe of eachsize (<5 ft deep) $/LF Sanitary sewer pipe of each size (5-10 ft deep)$/LF Sanitary sewer pipe of each size (>10 ft deep) $/LF Sanitary sewermanholes of each size (<5 ft deep) $/EA Sanitary sewer manholes of eachsize (5-10 ft deep) $/EA Sanitary sewer manholes of each size (>10 ftdeep) $/EA Sanitary sewer clean outs $/EA Sanitary sewer serviceconnections $/EA Sanitary sewerage lift stations $/EA Septic tanks orpackage treatment plants $/EA Sanitary sewer force main of each size$/LF

TABLE 4 Potable Water System Water distribution pipe of each size $/LFWater meters $/EA Valves of each size $/EA Fire Hydrants $/EA BackflowPreventer Valves $/EA

VI. Conceptual Solutions

Conceptual solutions to the land development problem are generatedutilizing the DTM, rules of selection, and cost model described above.These solutions are optimized using a heuristic problem-solvingstrategy, such as genetic algorithms, simulated annealing, neuralnetworks, hill climbing, Ant Colony optimization, Particle Swarmoptimization, tabu search, and other computerized evolutionarytechniques. In the present example, the system incorporates a geneticalgorithm (GA) such as that represented in the flow diagram of FIG. 11.As previously indicated, a high level problem statement in the presentexample is: developing a site for single pad commercial usage based onpredetermined cost measurements and budget constraints. The GA works toevolve a set of globally optimized solutions—each solution conceptuallylocating (within the site) the five cost measurements discussed above ina manner which is highly cost efficient, and which takes intoconsideration system and user constraints and user preferences.

Beginning at generation ‘0’, the first step in the GA is to create aninitial random population of conceptual solutions. Each solutioncomprises respective sets of (x,y,z) coordinates in the DTM representingthe exact location of the building pad, parking lot & access roads, thestorm water system, sanitary sewer collection system, and potable watersystem. This initial population may include thousands or more ofpotential solutions.

For each solution in the population, the five cost measurements aredefined, respectively, by certain predetermined quantities calculatedbased on the precise location of the measurement in the DTM. Forexample, the building pad and parking lot & access roads impact gradingvariables, such as: the total disturbed area, total volume of excavatedmaterial, volume of excavated rock, volume of excavated unsuitablematerial, volume of fill material, retaining wall area, parking area,concrete sidewalk area, length of curb and gutter, and slope surfacearea. Storm water system variables include, for example, volume ofunderground detention storage, length of RCP storm water pipe of eachsize, number of flared end sections of each size, volume of rip rap,number of storm water manholes of each size, number of curb inlets,number of drop inlets, number of storm water lift stations, and lengthof PVC storm water force main of each size. Sanitary sewer collectionsystem variables include, for example, length of PVC sanitary sewer ofeach size, number of sanitary sewer manholes of each size, number ofsanitary sewer clean outs, number of sanitary sewer service connections,number of sanitary sewer lift stations, number of septic tanks orpackage treatment plants, and length of PVC sanitary sewer force main ofeach size. The potable water system variables include, for example,length of PVC water distribution pipe of each size, number of watermeters, number of (cast iron) valves of each size, fire hydrants, andbackflow preventers.

Referring again to the diagram of FIG. 11, after creating the initialrandom population, the next step is to apply a fitness function whichquantitatively evaluates the fitness of each candidate solution. Thisstep involves first determining the engineering feasibility of thesolution, and whether the solution satisfies the rules of selectiondiscussed above. If the solution meets these threshold requirements, itis then scored for fitness utilizing the cost model and any applicablepenalties. If not, the solution is immediately discarded. An example ofa solution discarded for lack of engineering feasibility is one whichlocates the building pad over the parking lot and/or access roads. Thisdiscarded solution would also likely violate a rule of selectionrelating to the proximity of the parking lot to the building pad.Another example of a solution discarded for a rules violation would beone where the building pad encroaches the setback.

For those solutions meeting the above threshold requirements, a fitnessindex is assigned to each of the five cost measurements. In the presentexample, assume that this index is a scale of 1-9; one (1) representinga measurement with high cost/high penalties, and nine (9) representing ameasurement with low cost/low penalties. As previously stated, penaltiesare assigned to measurements which violate a user preference or “softconstraint”. For example, if a user desires the building pad (storefront) to face in a particular direction on the site in order tomaximize visibility, any deviation from the desired pad orientationbeyond a predetermined range would yield a penalty—or a minus score. Aperfect fitness score in the present system is a solution which producesa nine for each measurement, or a ‘99999’ score. Cost and penaltiesdetermine the fitness of each solution in the population. The cost ofeach measurement relates directly to its conceptual (x,y,z) location inthe DTM, and is calculated based on the cost model.

In calculating the fitness index, the cost component is scored based onthe measurement's ranking relative to other like measurements in thepopulation. Thus, for example, all cost measurements for the buildingpad in the 90th and above fitness percentile would receive a ‘9’ score,those in the 80th-89th fitness percentile would receive an ‘8’ score,and so on. From this cost-component score, deductions are made for anypenalties. This process is followed for all five cost measurements ineach candidate solution.

After scoring each solution in the population, the GA determines whethera known termination criterion is satisfied. In the present example, thetermination criterion is a preselected number of rounds or“generations”. Assuming that this criterion is not yet satisfied, thesystem then selects certain candidate solutions to be copied over intothe next generation. The GA can use many different techniques toaccomplish this; namely, an elitist selection, fitness-proportionateselection, roulette-wheel selection, scaling selection, tournamentselection, rank selection, generational selection, steady-stateselection, and hierarchical selection. Some of these methods aremutually exclusive, but others can be and often are used in combination.

According to the elitist selection, the most fit solutions of eachgeneration are guaranteed to be selected. In the fitness-proportionateselection, more fit individuals are more likely, but not certain, to beselected. The roulette-wheel selection is a form offitness-proportionate selection in which the chance of a solution beingselected is proportional to the amount by which its fitness is greateror less than its competitors' fitness. According to the scalingselection, as the average fitness of the population increases, thestrength of the selective pressure also increases and the fitnessfunction becomes more discriminating. This method can be helpful inmaking the best selection later on when all solutions have relativelyhigh fitness and only small differences in fitness distinguish one fromanother. In the tournament selection, subgroups of solutions are chosenfrom the larger population, and members of each subgroup compete againsteach other. Only one solution from each subgroup is then chosen toreproduce. In the rank selection, each solution in the population isassigned a numerical rank based on fitness, and selection is based onthis ranking rather than absolute differences in fitness. The advantageof this method is that it can prevent very fit individuals from gainingdominance early at the expense of less fit ones, which would reduce thepopulation's genetic diversity and might hinder attempts to find anacceptable solution. In the generational selection, the offspring of thesolutions selected from each generation become the entire nextgeneration. No solutions are retained between generations. In thesteady-state selection, the offspring of the solutions selected fromeach generation go back into the pre-existing population, replacing someof the less fit members of the previous generation. Some solutions areretained between generations. In hierarchical selection, solutions gothrough multiple rounds of selection each generation. Lower-levelevaluations are faster and less discriminating, while those that surviveto higher levels are evaluated more rigorously. The advantage of thismethod is that it reduces overall computation time by using faster, lessselective evaluation to weed out the majority of solutions that showlittle or no promise, and only subjecting those who survive this initialtest to more rigorous and more computationally expensive fitnessevaluation.

In the present example, a rank/elitist selection method chooses allcandidate solutions which have a cumulative score across all fivemeasurements of at least a certain minimum number; e.g., 25. In thiscase, for example, solutions with respective fitness scores of ‘55555’and ‘11888’ would be selected, while solutions scoring ‘33666’ and‘99111’ would not.

Once selection has chosen fit solutions, they are then randomly alteredin hopes of improving their fitness for the next generation. This randomalteration occurs through mutation and crossover. A solution is mutatedby slightly altering the (x,y,z) coordinates of any one or more of itscost measurements. Crossover entails choosing two solutions to swap oneor more measurements, thereby producing artificial “offspring” that arecombinations of their parents. With crossover, there is a transfer ofinformation between successful “individuals” —solutions that can benefitfrom what others have learned, and schemata can be mixed and combined,with the potential to produce an offspring that has the strengths ofboth its parents and the weaknesses of neither.

The above process is repeated until the prescribed number of generationshave been evolved. At that point, from the highest scoring solutionsgenerated by the GA, a further mathematical algorithm outputs multipleoptimally different alternatives for review and consideration by theuser. The output is preferably one or more computer-generated (CAD)drawings of the site plan indicating the various measurements for eachfit solution, and a written cost report itemizing and totaling all ofthe associated costs for developing the single pad commercial site. Theprocess discussed below ensures that the output solutions are optimallydifferent.

VII. Optimally Different Solutions

In the present example, an output algorithm is employed to interrogatethe chosen GA solutions and output to the user only those that areoptimally different. The primary goal of this algorithm is to generate asmall number (e.g., 6-8) of very different alternatives.

The pseudo-code algorithm may read as follows:

GA selects multiple fit solutions Repeat evaluate the (x,y,z)coordinates of all measurements in each fit solution select solutionswhich maximize the distance between coordinates of like measurementsUntil terminating condition.

In this case, the terminating condition would be reached once thedesired number of very different, fit solutions is achieved.Alternatively, the output algorithm may employ other terminatingcriterion.

As indicated above, the output documentation preferably comprises one ormore computer-generated drawings and a written Cost Report. An exampleof an output drawing for one optimized, fit solution is provided in FIG.12. The Solution Cost Report for this site is as follows:

SOLUTION COST REPORT Description Unit Quantity Unit Cost Total CostBuilding Subsystem Subtotal Building Cost: $0.00 Driveway Curb andGutter LF 198.35 $12.20 $2,419.89 Heavy Duty Asphalt Paving SY 501.25$19.00 $9,523.83 Subtotal Driveway Cost: $11,943.71 Grading Clearing andGrubbing Acre 2.04 $8,000.00 $16,336.43 Topsoil Stripping/Stockpiling CY549.09 $2.85 $1,564.89 Earth Excavation (Cut) CY 3,381.47 $2.85$9,637.19 Fill Placement CY 2,914.68 $2.85 $8,306.83 Borrow Material CY0 $2.85 $0.00 Waste Material Disposal CY 141.51 $6.00 $849.06 RetainingWall (21.8 ft) SF 4,104.30 $17.00 $69,773.16 Retaining Wall (6.2 ft) SF329.34 $17.00 $5,598.82 Finish Grading Acre 2.04 $14,520.00 $29,650.62Seeding and Mulching Acre 0.67 $1,600.00 $1,070.32 Subtotal GradingCost: $142,786.33 Parkinq Lot Curb and Gutter LF 951.55 $12.20$11,608.97 Standard Duty Asphalt Paving SY 5,826.50 $13.94 $81,221.46Subtotal Parking Lot Cost: $92,830.43 Potable Water System Water Pipe (1inches) LF 255.4 $14.50 $3,703.34 Connect to Existing Water Line (1″) EA1 $850.00 $850.00 Water Service Meter (1″) EA 1 $2,250.00 $2,250.00Building Control Valve (1″) EA 1 $0.00 $0.00 Subtotal Potable WaterSystem Cost: $6,803.34 Sanitary Sewer Collection System Sanitary SewerGravity Pipe (4″) LF 39.87 $20.05 $799.39 Sanitary Sewer Gravity Pipe(4″) LF 100.57 $21.35 $2,147.19 Sanitary Sewer Gravity Pipe (4″) LF167.19 $34.45 $5,759.68 Connection to Existing EA 1 $850.00 $850.00Sanitary Sewer Collection System Sewer Cleanout (4″) EA 5 $500.00$2,500.00 Subtotal Sanitary Sewer Collection System Cost: $12,056.25Storm Water Collection System Storm Water Gravity Pipe (18″) LF 66.95$24.00 $1,606.89 Storm Water Gravity Pipe (15″) LF 482.34 $21.10$10,177.28 Connection to Existing EA 2 $850.00 $1,700.00 Storm WaterCollection System Storm Water Manhole (60″) EA 1 $3,930.00 $3,930.00Storm Water Inlet (48″) EA 2 $1,760.00 $3,520.00 Storm Water Inlet (48″)EA 3 $1,335.00 $4,005.00 Storm Water Manhole (48″) EA 1 $1,760.00$1,760.00 Storm Water inlet (48″) EA 1 $3,930.00 $3,930.00 SubtotalStorm Water Collection System Cost: $30,629.77 Total Cost: $297,049.24

A computer-implemented land planning system and method are describedabove. Various details of the invention may be changed without departingfrom its scope. Furthermore, the foregoing description of the preferredembodiment of the invention and best mode for practicing the inventionare provided for the purpose of illustration only and not for thepurpose of limitation—the invention being defined by the claims.

1. A computer-implemented land planning system designed to generate atleast one conceptual fit and cost-optimized solution to a user-definedland development problem, said system comprising: means forelectronically creating at least one candidate solution to the landdevelopment problem, said candidate solution comprising a plurality ofinterrelated engineering measurements applicable in development of anundeveloped land site, and said plurality of engineering measurementsbeing selected from a group consisting of grading, building pad layout,and utilities; means for accessing cost data for said plurality ofengineering measurements; means for employing an iterative, heuristicproblem-solving strategy to manipulate the engineering measurements ofsaid candidate solution until at least one cost-optimized fit solutionto the land development problem is achieved, whereby a change relativeto one of said plurality of engineering measurements for said candidatesolution effects a change relative to another of said plurality ofengineering measurements for that candidate solution; and means foroutputting to a user documentation illustrating said cost-optimized fitsolution to the land development problem.
 2. A computer-implemented landplanning system designed to generate at least one conceptual fit andcost optimized solution to a user-defined land development problem, saidsystem comprising: means for accessing land development constraints foran undeveloped land site; means for electronically creating at least onecandidate solution which meets the land development constraints of theundeveloped land site, said candidate solution comprising a plurality ofinterrelated engineering measurements, and said plurality of engineeringmeasurements being selected from a group consisting of grading, buildingpad, parking lot and access roads, storm water collection system,sanitary sewer collection system, and potable water system; means foraccessing cost data for said plurality of engineering measurements;means for calculating a fitness score for said candidate solution basedon said cost data for said engineering measurements; means for employingan iterative, heuristic problem-solving strategy for manipulating saidengineering measurements of said candidate solution to achieve anincreased fitness score until at least one cost-optimized fit solutionis achieved, whereby a change relative to one of said plurality ofengineering measurements for said candidate solution effects a changerelative to another of said plurality of engineering measurements forthat candidate solution; and means for outputting to a userdocumentation illustrating said cost-optimized fit solution to the landdevelopment problem.
 3. A computer-implemented land planning methoddesigned to generate at least one conceptual fit and cost-optimizedsolution to a user-defined land development problem, said methodcomprising: electronically creating at least one candidate solution tothe land development problem, the candidate solution comprising aplurality of interrelated engineering measurements applicable indevelopment of an undeveloped land site, and the plurality ofengineering measurements being selected from a group consisting ofgrading, building pad layout, and utilities; employing an iterativeheuristic problem-solving strategy for manipulating the engineeringmeasurements of the candidate solution until at least one cost-optimizedfit solution to the land development problem is achieved, whereby achange relative to one of the plurality of engineering measurements forthe candidate solution effects a change relative to another of theplurality of engineering measurements for that candidate solution; andoutputting to a user documentation illustrating the cost-optimized fitsolution to the land development problem.